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Are we what parties we support? Personality traits and party support in a multi‐party system.

  • Published In: Analyses of Social Issues & Public Policy, 2023, v. 23, n. 3. P. 652 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Björkstedt, Hanna E.; Herne, Kaisa M. 3 of 3

Abstract

There is relatively little evidence about the association of personality to political behavior in multi‐party systems. We analyze the association of two personality traits to party support in a multi‐party system, where parties are differently aligned along the economic left‐right axis and the GAL‐TAN axis, that extends from green, alternative and libertarian to traditional, authoritarian and nationalist values. Machiavellianism refers to a manipulative and cynical personality, whereas Perspective‐Taking is a tendency to see things from others' perspective. We ask whether the left‐right or the GAL‐TAN axis is more relevant to the association between the personality traits and party support. We observed that the nationalist and conservative Finns party supporters score higher on Machiavellianism and lower on Perspective‐Taking in comparison to the environmental and liberal Greens party supporters. These two parties are located at the opposite ends of the GAL‐TAN axis. We do not see corresponding results on parties at the opposite ends of the left‐right axis. The result suggests that personality traits may be more relevant for supporting parties that are best characterized by their location on the GAL‐TAN axis. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Analyses of Social Issues & Public Policy. 2023/12, Vol. 23, Issue 3, p652
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:1529-7489
  • DOI:10.1111/asap.12366
  • Accession Number:174238112
  • Copyright Statement:Copyright of Analyses of Social Issues & Public Policy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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